Professor David Skillicorn Queen’s University of Kingston, Canada, presented a joint NEST and Computer Science Department seminar entitled: "Enriching Social Network Analysis for Adversarial Settings" hosted by Prof. Boleslaw Szymanski on April 17, 2017. The talk focuses on adversarial aspects of human interactions. Although each individual thinks that the decision to form a link in a social network is an autonomous, local decision, we know that this isn’t so. Larger forces are involved: human mental limitations (Dunbar’s Number), social balance that induces triangle closure and resists certain signed triads (Georg Simmel), and the flow of properties ‘over the horizon’ within networks (Christakis). Social network analysis has three phases: aggregating individual link decisions, understanding the global structure that results, and revisiting each link in the context of this global structure; and both the second and third phases provide insights. The relationships associated with links are much richer than most social network analysis recognises: relationships can be of qualitatively different types (friend, colleague), directed (so the intensity one way is different from the intensity the other), signed (both positive and negative, friend or enemy), and dynamic (changing with time). All of these extra properties, and their combinations, can be modelled using a single scheme, creating an expanded social network, which can then be analysed in conventional ways (for example, by spectral embedding). Social network analysis is especially useful in adversarial settings (where the interests of those doing the modelling and [some of] those being modelled are not aligned) because each individual cannot control much of the global structure. I will illustrate how this pays off in law enforcement and counterterrorism settings.